Federated learning (FL) [1]}, which enables multiple data owners to learn a machine learning (ML) or deep learning (DL) model with joint efforts, is increasingly used in various applications such as medical image analysis [2]}, [3]}, [4]}, word predictions [5]}, [6]}, [7]} and recommendation systems [8]}, [9]}. In this manner, a large amount of private data from multiple participants is available to generate accurate and reliable ML models. However, FL protects participants' personal data privacy at the sacrifice of holding the model also privately. Compared with traditional centralised model training, all parties in FL have access to the global model, increasing the risk of model leakage. Since training an FL model requires a large number of clients and computations, copyright protection of FL models becomes an important issue.
